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引用次数: 1
摘要
本文利用基于转换模型的现代方法,解决了基于方面的情感分析(ABSA)任务的多语言和语言不可知论方法的重要问题。我们提出了一个基于波兰语AspectEmo数据集的自动翻译和描述方面极性标签的跨语言迁移的新数据集。结果是将MultiAspectEmo数据集翻译成其他五种语言:英语、捷克语、西班牙语、法语和荷兰语。在本文中,我们还提出了原始的Tr Asp(基于transformer的Aspect Extraction and Classification)方法,该方法在ABSA任务中明显优于文献中的方法。此外,我们提出了该方法的多语言和语言不确定变体,并在MultiAspectEmo和SemEval2016数据集上进行了评估。我们还为ABSA任务测试了各种语言模型,包括压缩模型,这些模型提供了有希望的结果,同时显著减少了推理时间和内存使用。
MultiAspectEmo: Multilingual and Language-Agnostic Aspect-Based Sentiment Analysis
The paper addresses the important problem of multilingual and language-agnostic approaches to the aspect-based sentiment analysis (ABSA) task, using modern approaches based on transformer models. We propose a new dataset based on automatic translation of the Polish AspectEmo dataset together with cross-lingual transfer of tags describing aspect polarity. The result is a MultiAspectEmo dataset translated into five other languages: English, Czech, Spanish, French and Dutch. In this paper, we also present the original Tr Asp (Transformer-based Aspect Extraction and Classification) method, which is significantly better than methods from the literature in the ABSA task. In addition, we present multilingual and language-agnostic variants of this method, evaluated on the MultiAspectEmo and also the SemEval2016 datasets. We also test various language models for the ABSA task, including compressed models that give promising results while significantly reducing inference time and memory usage.